Analysis vs. analytics: A defining moment?

You might hear the terms clinical data analysis and clinical data analytics bandied about rather frequently. But one question might percolate to the surface: What is the difference between analysis and analytics? Or are they synonymous?

At least one non-healthcare data strategist has purported that data analysis involves drawing conclusions from a single data set; data analytics involves drawing conclusions from multiple data sets, including correlations between them. Another suggests analysis is part of analytics.

“In my view, clinical data analytics is the use of big data sets to drive change. Analytics provide the opportunity to look at a problem from many different angles and perspectives (provider level, practice, service line, hospital, or enterprise) to drive change at the appropriate level and at the appropriate target. Clinical data analysis suggests a more finite analysis that can be used to assess a specific opportunity identified from the clinical analytics.”

“Providers have spent millions on big data. But nobody started by asking: What decisions are we trying to influence? Health systems that will drive the most change are those that leverage a data analytic and the analysis from it to make a decision to improve the bottom line of the hospital. Start by focusing on the operating decisions you want to make from the data that’s being collected. It’s time to reverse-engineer the thinking.”

“As a consumer of healthcare news and reports, we have learned to be careful about terminology, especially when it comes to the latest healthcare buzz words like clinical data analysis or analytics. Analysis implies an act of analyzing something, an action that is taken based on data. So, for example, clinical data analysis would include using a data set of patients with one specific diagnosis and comparing their outcomes based on specified factors, such as location of care, time in the OR, implant brand, and so on. Analytics, on the other hand, suggests a thing that exists; a data set that has already been analyzed or tools that assist in the analysis process. For example, clinical data analytics could include a dashboard, benchmarks, or reporting tools based on Boolean logic or well-established algorithms. These tools can be both the outcome of analysis and the tools for more analysis.”

“From our perspective, analysis indicates the action of reviewing data to try to solve or understand a problem; analytics indicates the underlying program that enables an analysis to occur. However, the variation of many opinions and determining a concrete difference between analysis and analytics among the diverse set of viewpoints can distract from identifying new and better ways to improve care. What’s more important is that the data collected is meaningful and solves the (often hidden or unknown) challenges clinicians face when making decisions that affect patient care. We should be constantly looking for innovative ways to use data to drive decisions and understand the complexities of care. While we often talk about optimizing pricing and deciding which products to purchase, we don’t talk as much about the processes that surround them. For example, when a nurse leaves her unit looking for supplies in another unit due to inappropriate inventory, the ability to provide one-on-one care with a patient is lost. A good supply chain analytics program should have the ability to evaluate products as well as processes.”

“At Sentry, we believe that data analysis is the action that a person performs, using the tools and resources at his or her disposal. In contrast, data analytics is the collection of tools and methodologies that the user employs to drive improvement in their business. We believe that data analytics should be actionable, able to be used by both front-line staff as well as leadership at all levels, and delivered in a timely, accurate, and reliable way. When these requirements are met, then the data analysis performed leads to decisions and actions that benefit the organization, the operational stakeholders, and most importantly — the patients.”

“Clinical data analysis is a subset of clinical data analytics. Clinical data analysis is a specific analysis targeted at a specific question. It’s used to gain deep insights and guide improvement strategies around a specific issue. Clinical data analytics on the other hand, looks at issues at more of a macro level such as providers by practice, service line, individual hospital or health system.”

“I agree with both. But in my opinion, the semantics are irrelevant, the important thing is that Supply Chain is involved in analytics and analysis — both internally on supply data and collaboratively on combined clinical, financial and supply data sets. This analysis can’t be outsourced to information technology or finance departments who don’t understand the nuances of tiered contracts, rebates, consignment items and clinical functional equivalents.

“In many hospitals, Supply Chain struggles to access even basic supply data. It’s stuck in outdated software systems waiting in the upgrade line, distant to the priority clinical systems. Supply Chain leaders have attempted to overcome this hurdle by outsourcing analytics to third party vendors, but these engagements are only as good as the accompanying change management consulting services. My suggestion is to start simple. Find a willing clinical partner, identify a decision point where analysis can be inserted, find the clinical outcomes measures, and check to be sure the data is clean and organized. The return on investment from this initial project will fuel further incorporation of analytics in your Supply Chain.

“And, a final reminder, the prerequisites to any supply related clinical analytics project are a clean item master, clean vendor master, and a linked item and chargemaster crosswalk.”

“Analysis refers to a process or method of studying and investigating data sets. Analytics, on the other hand, is evaluating patterns and meaningful information gathered from analysis of data. However, analysis and analytics work hand in hand. When drawing conclusions from large data sets, it is easy to work with data that is present. The struggle in healthcare is understanding the gaps in data and the impact of those gaps. Our future is predicated on providing better outcomes at lower costs and delivering the best possible care to our communities. As part of data analysis, uncovering cracks in the foundational financial and operational data sets will allow organizations to embrace the change required to fill those missing data components and focus on patterns to drive operational efficiencies, reduce financial risk and enhance the quality of care received by their communities.”

“From my perspective, data analysis refers to hands-on data exploration and evaluation, while data analytics is a broader term and includes data analysis as a fundamental activity. Analytics represents the science of the analysis. While a data analyst can provide analysis, analytics, including the interpretation, definition of limitations, validity, and visualization is best performed by a data scientist. In combining clinical and operational data, it is imperative that the data mapping is comprehensive and reliable in order to accurately correlate the data and provide actionable insights.”